Methods and systems for context-aware entity correspondence and merging

ABSTRACT

A computer-based method for correlating relevant information from multiple entities based on contextual correspondence is described. The method includes receiving, at a computer, information relating to a plurality of the multiple entities, the information including data features and context features, correlating the data features utilizing one or more algorithms running on the computer, correlating the context features utilizing one or more algorithms running on the computer, and aggregating the plurality of the multiple entities based on both a correspondence of the data features and a correspondence of the context features for at least one of storage in a memory associated with the computer and output as data from the computer.

BACKGROUND

The field of the disclosure relates generally to context awareinformation collection and aggregation in a dynamically changing anddistributed environment, where information is needed by differentinformation consumers for different purposes. Specifically, thedisclosure is directed to methods and systems for context-aware entitycorrespondence and merging.

Existing information aggregations are static and current approachesdon't address dynamic information aggregation. In contrast, context isany information used in realization of the meaning of an entity and anyinformation used to characterize the situation of an entity. Examples ofcontext information may include: location, identity, time and activity.Context answers fundamental questions such as what is occurring in thesituation, where you are, who you are with, and what objects are around.Additionally, context refers to the current values of specificingredients that represent a user's activity/situation.

Entity resolution (ER) approaches do not analyze the context features.Specifically, a disambiguation quality associated with ER approachesfrequently depends on the context in which they are employed. Anotherapplication of the proposed approach is that if the context can becaptured then this knowledge can be utilized to significantly improveperformance of an ER ensemble.

BRIEF DESCRIPTION

In one aspect, a computer-based method for correlating relevantinformation from multiple entities based on contextual correspondence isprovided. The method includes receiving, at a computer, informationrelating to a plurality of the multiple entities, the informationincluding data features and context features, correlating the datafeatures utilizing one or more algorithms running on the computer,correlating the context features utilizing one or more algorithmsrunning on the computer, and aggregating the plurality of the multipleentities based on both a correspondence of the data features and acorrespondence of the context features for at least one of storage in amemory associated with the computer and output as data from thecomputer.

In another aspect, one or more computer-readable storage media havingcomputer-executable instructions embodied thereon are provided, whereinwhen executed by at least one processor, the computer-executableinstructions cause the at least one processor to receive informationrelating to a plurality of the entities, the information including datafeatures and context features, separately correlate the data featuresand the context features and aggregate the plurality of entities basedon both a correspondence of the data features and a correspondence ofthe context features.

In still another aspect, a context aware information collection andaggregation method is provided that includes defining an entity,generating a context data structure, the context data structurecorresponding to a predefined context definition, receiving context databased on a context definition including one or more data types and oneor more parameters for each data type based on entity properties, entitypositions and entity types, formatting data for an entity separatelyfrom the context data, and outputting a result based on the context dataand said entity data according to a set of entity criteria.

The features, functions, and advantages that have been discussed can beachieved independently in various embodiments or may be combined in yetother embodiments further details of which can be seen with reference tothe following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a depiction of a context-based entity which is represented byentry data features and entry context features.

FIG. 2 is a representation of depictions of three context-based entitiesand grouping of the entities at different times and locations, based oncontext.

FIG. 3 is a diagram of a data processing system.

FIG. 4 is a depiction of a context-based entity factory process.

FIG. 5 is a depiction of a context-aware entity correspondence process.

FIG. 6 is a flow diagram illustrating the combination of thecontext-based entity factory process of FIG. 4 and the context-awareentity correspondence process of FIG. 5.

DETAILED DESCRIPTION

A number of inter-related technical challenges are addressed herein,ranging from issues associated with context modeling and representation,context acquisition and inference, contextual correspondenceidentification, and handling context incompleteness/uncertainty, tounderstanding the role/impact on “organic” cognitive processes andassessing the efficacy of context-aware entity correspondence and merge.The described embodiments provide a modular, flexible, extensiblecontext meta-model to represent, correlate, and transform context dataamong different models and data instances, methods to dynamically link,group, aggregate, and merge contextually relevant information artifacts,semantic meaning extraction, contextual correlation derivation, andpredict information needs based on context changes, and processing ofcontextual events.

The described embodiments address the problems related to thecorrelation of relevant information from multiple sources based on thecontextual correspondence of multiple information entities. Theembodiments further address the linking and/or merging of these multipleinformation entities together to better meet the total information needsof users in specific situations.

Drawing on the definition of context provided above, context-awarenessis an ability of a system to sense, interpret, and react to changes inthe environment in which a user is situated. For example, a particularinformation entity co-exists together with other information entities ina given information space. Given the particular information entity, itscontextual background usually shapes the meaning and the value of theparticular information entity, as well as any correlations theparticular information entity may share with the other informationentities. In order to manage information along with its meanings,values, and correlations with other entities, an appropriate contextualenvironment of an information entity is characterized in some manner.The embodiments relating to context-aware entity correspondence andmerging (CECM) represent information entities by their data and contextfeatures, which are referred to as context-based entities (CE).

In order to manage information it must be characterized in some manner.Entity context characterizes an entity, and entity is represented by twotypes of entity features data and context. Such entity characterizationsinclude, but are not limited to, entity identity, entity type, time,location and activity. As implied in the preceding sentence, other typesof context may be included. Entity context features are dynamic andassumed to be constantly changing and therefore may not be persistentlystored in a database. However, without losing any generality, and forsimplicity, it is assumed entity context features are stored in apersistent data repository, as represented in FIG. 1. Referringspecifically to FIG. 1, a context-based entity 10 is represented byentry data features 12, 14, and 16 and entry context features 22, 24,and 26.

A CECM solution correlates and aggregates multiple context-basedentities based on their contextual correspondence, and therefore extendsthe one-dimensional (data feature only) information representation to amulti-dimensional (with various context features) informationrepresentation. As a result, a CECM solution will provide more optionsfor decision makers to reduce information overload by filtering morerelevant, precise, and ready to consume information from a largeinformation space, tailored by, for example, an area of operation, acommander, a time period, and a given mission, each of which representsa specific context feature of the original information entity.

Context-aware Entity Correspondence (CAC). Built on top of the describedcontext modeling and reasoning capabilities, CAC dynamically links,groups, aggregates, and merges contextually relevant information. CACalso calculates contextual relationships, and composes new informationartifacts that merge multiple existing artifacts together.

Context-aware entity correspondence is concerned with the association ofindividual entities based on entity context and context model orontology. The relationships among entities are influenced by theircontexts. Relationships among context-based entities can be dynamicallyestablished by their shared common context.

In CECM, the entities' correspondence is represented by a numericalstrength indicative of context features compatibility between entities.FIG. 2 includes depictions of three context-based entities ce1, ce2, andce3. At time t1, location l1, ce1 and ce2 maybe correspond with highvalue but not correspond with ce3. In one such example, ce1 couldrepresent a person “John” and the temporal context feature might by “atlunch time”, ce2 represents “the nearest restaurant”, and ce3 is “thenearest electronic store”. At a different time t2 and location l2; ce1may have a higher correspondence with ce3 than with ce2. Similarly at atime t3 and location l3, ce1, ce2 and ce3 all have the samecorrespondence value. An example may be where John would like to pick upa sandwich and then do some shopping before heading home.

In yet another example, given a person's contextual information such astime, location, user identity, and his/her mission in a battle spaceenvironment, multiple context-based entities about units (friendly orhostile), movements, activities, and damage assessment reports near thelocation may be linked and aggregated to further reveal opportunities,mitigate risk, and predict early warnings. The context of acontext-based entity may change (e.g. used by different consumers, fordifferent purposes, at different time/locations) and the set of itsrelated context-based entities may thus change accordingly even thoughthe data feature of the information object remains the same. CECMprovides context aware information aggregation to identify, expose,derive, and manage meaningful relationships among context-basedentities, to provide decision makers with a more comprehensive andintegrated view of real-time battle space status, as well as to predicttheir future information needs.

Advantages of Context-Aware Entity Correspondence and Merge (CECM)

Existing approaches for information aggregation or informationde-duplication have focused on the “static information value” only.However, if another dynamic aspect is introduced, that is, even if theinformation value remains the same, the merge or aggregation maygenerate different results because of the surrounding contextualinformation is different. A CECM system investigates the problem ofcorrespondence entities based on contextual features of entities,semantic model and rules and then merges the entities based on user'spolicies or rules. This is sometimes referred to as context awareinformation aggregation.

The CECM solution addresses: a modular, flexible, extensible meta-modelto represent, correlate, and transform data among different models anddata instances, methods to dynamically link, group, aggregate, and mergecontextually relevant context-based entity, and context-based entityautomated meaning extraction, contextual correlation derivation, andpredict future information needs by changing context.

CECM has some similarity to Entity Resolution (ER) but is fundamentallydifferent. For example, where in ER there are two or more sourcescontaining records representing same real-world entities (e.g.,customers). However, the records representing the same entity may havediffering information, e.g., one record may have the address misspelled,another record may be missing some fields. An entity resolutionalgorithm attempts to identify the matching records from multiplesources (i.e., those corresponding to the same real-world entity), andmerges the matching records as best it can.

Context-Aware Model

Different types of context are represented by one or more models. Forexample, location context may be expressed as a geometric modelrepresenting location as an n-dimensional space and a symbolic modelrepresenting location using logical real-world entities such asbuildings, streets, cities, or system-defined elements like economicalzones. The choice of an appropriate context model may vary amongdifferent information providers and information consumers. A combinedmodel provides easy correlation, conversion, and derivation between oneand another. Context-aware entity correspondence and merge provides acontext meta-model mechanism, which leverages many existing contextualontologies (e.g. time, location, workflow, etc), with mappings andconversions represented by both ontological predicates and pluggablereasoning services. These services calculate information relationships(e.g. real-time spatial relationships such as distance/travel timebetween two units) and compose new information objects that mergemultiple existing information objects together.

By binding temporal and contextual information relevancy, informationregarding anticipated future context-based entity needs (e.g. futurecomplex subscription) is developed. Predictive information managementservices leverage reasoning to infer information need, and when bound toQuality of Service services aware of temporal network and informationresource topologies, the timeliness of relevant information whileminimizing resource consumption is maximized.

Context-aware entity correspondence and merge relies on user-definedfunctions that (a) compare fields or records to determine if they match.Such matches can be deterministic and probabilistic or based on fuzzylogic matching algorithms. Context-aware entity correspondence and mergeleverage from existing ER strategies and algorithms but incorporate morecontext features to enable dynamic ER according to ever changingsurrounding environment.

In general, fuzzy logic resembles human reasoning in its use ofapproximate information and uncertainty to generate decisions. Inrelation to matching, the term is used loosely to describe the approachthat relies on rules that are imprecise rather than precise and operateson data with boundaries that are not sharply defined.

Deterministic matching gives equal weight to the different types ofinformation a record may contain. For example, a deterministic approachmight place equal reliance on a match between the location names on tworecords or a match between two times.

Probabilistic matching exploits the statistical probability that a matchon particular items is more or less likely to indicate that the recordsare match based on the existing context. For example, date informationis subject to errors made by a mistake on a single digit, and the numberof possible birth dates is relatively small. Names, in contrast, aremore likely to be recognizable even if a single error is made.Probabilistic matching thus allows assigning appropriate weights todifferent attributes and then compares the total score to the thresholdthat defines a successful match.

To address the context-aware entity correspondence and merge, some ofthe techniques form entity resolution algorithms and utilizeuser-defined functions that (a) compare context features to determine ifthey match (but not represent the same real world entity), and (b) mergematching records.

A context-aware entity correspondence and merge solution correlates andaggregates multiple context-based entities based on their contextualcorrespondence, and therefore extends the one-dimensional (data featureonly) information representation to a multi-dimensional (with variouscontext features) information representation. As a result, context-awareentity correspondence and merge provides more options for decisionmakers to reduce information overload by filtering more relevant,precise, and ready to consume information from a large informationspace, tailored by, for example, an area of operation, a commander, atime period, and a given mission, each of which represents a specificcontext feature of the original information entity.

The description of the different advantageous embodiments has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different advantageousembodiments may provide different advantages as compared to otheradvantageous embodiments. The embodiment or embodiments selected arechosen and described in order to best explain the principles of theembodiments, the practical application, and to enable others of ordinaryskill in the art to understand the disclosure for various embodimentswith various modifications as are suited to the particular usecontemplated.

Turning now to FIG. 3, a diagram of a data processing system is depictedin accordance with an illustrative embodiment. In this illustrativeexample, data processing system 300 includes communications fabric 302,which provides communications between processor unit 304, memory 306,persistent storage 308, communications unit 310, input/output (I/O) unit312, and display 314.

Processor unit 304 serves to execute instructions for software that maybe loaded into memory 306. Processor unit 304 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 304 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 304 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 306 and persistent storage 308 are examples of storage devices. Astorage device is any piece of hardware that is capable of storinginformation either on a temporary basis and/or a permanent basis. Memory306, in these examples, may be, for example, without limitation, arandom access memory or any other suitable volatile or non-volatilestorage device. Persistent storage 308 may take various forms dependingon the particular implementation. For example, without limitation,persistent storage 308 may contain one or more components or devices.For example, persistent storage 308 may be a hard drive, a flash memory,a rewritable optical disk, a rewritable magnetic tape, or somecombination of the above. The media used by persistent storage 308 alsomay be removable. For example, without limitation, a removable harddrive may be used for persistent storage 308.

Communications unit 310, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 310 is a network interface card. Communications unit310 may provide communications through the use of either or bothphysical and wireless communication links.

Input/output unit 312 allows for input and output of data with otherdevices that may be connected to data processing system 300. Forexample, without limitation, input/output unit 312 may provide aconnection for user input through a keyboard and mouse. Further,input/output unit 312 may send output to a printer. Display 314 providesa mechanism to display information to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 308. These instructions may be loaded intomemory 306 for execution by processor unit 304. The processes of thedifferent embodiments may be performed by processor unit 304 usingcomputer implemented instructions, which may be located in a memory,such as memory 306. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 304. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 306 or persistentstorage 308.

Program code 316 is located in a functional form on computer readablemedia 318 that is selectively removable and may be loaded onto ortransferred to data processing system 300 for execution by processorunit 304. Program code 316 and computer readable media 318 form computerprogram product 320 in these examples. In one example, computer readablemedia 318 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 308 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 308. Ina tangible form, computer readable media 318 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 300. The tangibleform of computer readable media 318 is also referred to as computerrecordable storage media. In some instances, computer readable media 318may not be removable.

Alternatively, program code 316 may be transferred to data processingsystem 300 from computer readable media 318 through a communicationslink to communications unit 310 and/or through a connection toinput/output unit 312. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunications links or wireless transmissions containing the programcode.

In some illustrative embodiments, program code 316 may be downloadedover a network to persistent storage 308 from another device or dataprocessing system for use within data processing system 300. Forinstance, program code stored in a computer readable storage medium in aserver data processing system may be downloaded over a network from theserver to data processing system 300. The data processing systemproviding program code 316 may be a server computer, a client computer,or some other device capable of storing and transmitting program code316.

The different components illustrated for data processing system 300 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 300. Other components shown in FIG. 3 can be variedfrom the illustrative examples shown.

As one example, a storage device in data processing system 300 is anyhardware apparatus that may store data. Memory 306, persistent storage308 and computer readable media 318 are examples of storage devices in atangible form.

In another example, a bus system may be used to implement communicationsfabric 302 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, without limitation, memory 306 ora cache such as that found in an interface and memory controller hubthat may be present in communications fabric 302.

The data processing system 300 of FIG. 3 is but one example of a dataprocessing system upon which the described embodiments may be executed.The methods described herein may be encoded as executable instructionsembodied in a computer readable medium, including, without limitation, astorage device or a memory area of a computing device as describedabove. Such instructions, when executed by one or more processors, causethe processor(s) to perform at least a portion of the methods describedherein. Also, and as utilized herein a “storage device” is understood tomean a tangible article, such as a hard drive, a solid state memorydevice, and/or an optical disk, that is operable to store data, such ascomputer-executable instructions.

Turning now to FIG. 4, a context-based entity factory process 400 isdepicted. Input data 402 is received by a context-based entity factory404. Within factory 404, a domain ontology 410 is defined and meta data412 are defined. Specifically, with respect to domain ontology 410, theontology is analyzed and defined and a context aware model is modified.With respect to meta data 412, the meta data is analyzed and definedabout data content and context and stored in a repository, such that thecontext-based entity factory 404 can provide context and time relevantdata 420 as an output.

In the context-based entity factory process 400 an extensible contextmodeling mechanism is developed that can incorporate, correlate andtransform dynamic contextual facts, such as temporal-spatialcharacteristics, as well as task-relevant information, among differentmodels and data instances. Different types of context are represented byone or more models, for example, by ontologies and/or lower-level datastructures/models. The choice of an appropriate context model may varyamong information providers/consumers. Therefore a combined modelmanagement mechanism is provided with easy correlation, conversion, andderivation between one and another.

More specifically, a context meta-model mechanism is included that workswith existing contextual ontologies (e.g. time, location, missionplanning, etc) and provides mapping among one and another. A genericcontext model management/mapping formalism is provided for importing theexisting models, and enriched semantic understanding of contextualcontent, enable contextual alignments, and perform progressive amendmentin response to the dynamically changing environment.

The meaning of various contextual data is usually hidden or highlyembedded in various data systems, creating one of the major barriers fora truly shared understanding among information consumers. To addresssuch a shortcoming, ontologies and algorithms are utilized to enrich thesemantics of contextual content, by means of tagging data models anddata instances with context ontologies. The tagging and mappingmechanism also provides varying degrees of domain-customized generality(e.g. conceptual grouping) and specialization (e.g. approximation viauncertainty) to deal with the dynamically changing information space.

The “noisy” factor of original data entry, such as errors, duplicates,and missing values (e.g. sensor readings) may invalidate existingcontextual mappings and further cause failure of reasoning engine toderive secondary context. New information sources may also joinconstantly, and the new data may not fit well with existing mappings.Conditional contextual alignments, uncertainties of contextualsimilarity and correspondence, as well as developing merging andconsistency checking axioms for real-time alignment, validation andprogressive amendment in response to changes and exceptions areincorporated. In addition, semantic rules are utilized tocompose/decompose existing alignments within a hierarchical structure,as well as append/relax alignment conditions and/or confidence levelsfor approximations, to greatly increase the adaptability and reliabilityof context data management and dissemination in dynamic environments.

The information producers/consumers roles vary greatly under variouscircumstances and contexts. Each agent takes a role in handling thecontext where importance is indicated by the priority. The extensiblecontext model also applies “user-centric” confidence values (i.e.depicting the relevancy to a particular task/mission) to low-levelcontextual facts, which can be propagated to high-level contextualinformation according to the specific roles that different informationproducers/consumers are playing in order to improve the effectivenessand efficiency of contextual reasoning capability as detailed herein.

In FIG. 5, the context and time relevant data 420 of FIG. 4 is utilizedas an input into a context-aware entity correspondence process 500. Inaddition to the context and time relevant data 420, contextual events502 are input into a context-aware entity correspondence engine 504.Within the engine 504, contextual events are processed 510, to determinecontextual matters such as time, location, identity, tasks, and anyapplication specific context. The processed 510 events meta data arematched 512 with context-based entity meta data based on context. Themeta data is analyzed and defined about data content and context andstored in a repository, such that the context-aware entitycorrespondence engine 504 can provide, as output 520, context based datafeatures and context based context features.

Process 500 receives context data based on a context definition,including one or more data types and one or more parameters for eachdata type based on entity properties, entity positions, and entitytypes. Data for output 520 is formatted for an entity separately fromcontext data.

One goal is to develop a reasoning capability to infer higher-levelknowledge (e.g. values, implications, ‘what if changed’ assumptions,hypotheses, etc.) about context from lower-level contextual properties,which can be derived from available data or low-level informationsources (e.g. sensors) distributed across the network. In heterogeneousand dynamic application domain, effective context modeling and reasoningare important if implicit contexts are to be represented to enabledistributed reasoning, decision making and collaboration. For example,when mission planning artifacts are bound by temporal constraints (e.g.make a decision within 15 minutes) and an information resource limit(e.g. UAV route and camera settings to derive UAV coverage) effectivecontext modeling and reasoning can maximize the timeliness of decisionmaking while minimizing resource consumption.

The effectiveness of existing approaches for distributed reasoningsignificantly degrades when scale goes up. To address, a layered contextmodel is utilized, in which higher-level context models can be inferredfrom lower-level context models, as well as distributed reasoning basedon priority. The layered context modeling is based on multidimensionalspace. In the lowest layer, a context model is inferred from theoriginal context information, while in higher layers, the context modelis inferred from the adjacent lower layer, without using the originalcontext information. The reasoning does not involve all the agents'perspectives, but only the highly relevant ones based on priority.Considered is the circumstance where there exists some differences inthe roles of the agents. If the priority of an agent is low, itscontribution in the merged model will be small and less resource will bespent on updating its context information. Therefore, the approachesdescribed herein decrease the amount of network traffic the system needsto process. Furthermore, this approach makes the system more stable andflexible in context adaptation.

The “user-centric” confidence values can increase the trust andstability of distributed reasoning within merged context models. If thepriority of an agent is low based on its confidence value, less resourceis used for updating its information. Furthermore, only when agents'contributions are greater than certain thresholds (i.e. derived from apredictive model of decision making and behavioral analysis),information will be processed to improve the resource optimization indistributed reasoning. Hence, minimizing the resources used to updateagents' context information, making context adaptation more stable andflexible, especially in a competing and resource constrained networkedenvironment.

FIG. 6 is a flow diagram 600 illustrating the combination of thecontext-based entity factory process 400 and the context-aware entitycorrespondence process 500. Specifically output of the context entityfactory 404 and contextual events 502 are combined within thecontext-aware entity correspondence engine 504, which provides contextbased data features and context based context features, collectivelyreferred to as context aware information 602.

The CECM embodiments described herein address a modular, flexible, andextensible meta-model to represent, correlate, and transform data amongdifferent models and data instances. Further, methods to dynamicallylink, group, aggregate, and merge contextually relevant context-basedentities are provided as are automated meaning extraction, contextualcorrelation derivation, and prediction of future information needs bychanging of context.

The embodiments address known problems related to the correlation ofrelevant information from multiple sources based on the contextualcorrespondence of multiple information entities so as to link and/ormerge those entities together to better meet total information needs inspecific situations.

In summary, the CECM solution described herein correlates and aggregatesmultiple context-based entities based on their contextualcorrespondence, and therefore extends the one-dimensional (data featureonly) information representation to a multi-dimensional (with variouscontext features) information representation. As a result, more optionsfor decision makers to reduce information overload by filtering morerelevant, precise, and ready to consume information from a largeinformation space, tailored by, for example, an area of operation, acommander, a time period, and a given mission, each of which representsa specific context feature of the original information entity.

This written description uses examples to disclose various embodiments,which include the best mode, to enable any person skilled in the art topractice those embodiments, including making and using any devices orsystems and performing any incorporated methods. The patentable scope isdefined by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

What is claimed is:
 1. A computer-based method for correlating relevantinformation from multiple entities based on contextual correspondence,said method comprising: receiving, at a computer, information relatingto a plurality of the multiple entities, the information including datafeatures and context features; correlating the data features utilizingone or more algorithms running on the computer; correlating the contextfeatures utilizing the one or more algorithms running on the computer;aggregating the plurality of the multiple entities based on acorrespondence of the data features, a correspondence of the contextfeatures, and at least one context model for at least one of storage ina memory associated with the computer and output as data from thecomputer, wherein the at least one context model includes temporalinformation associated with the context features; receiving a firstcontextual event including temporal information at a first locationassociated with the first contextual event; receiving a secondcontextual event including temporal information at a second locationassociated with the second contextual event, wherein the firstcontextual event is different from the second contextual event and thefirst location is different from the second location; matching aplurality of entities based on the received contextual events;determining a correspondence value for the matched plurality of entitiesbased on a comparison of at least one of the data features or contextfeatures in light of the temporal information of the first contextualevent and the first location; dynamically updating the correspondencevalue for the matched plurality of entities based on a comparison of atleast one of the data features or context features in light of thetemporal information of the second contextual event and the secondlocation; and outputting the matched plurality of entities that exceed apredetermined correspondence value.
 2. The method according to claim 1further comprising storing the context features associated with anentity in a repository.
 3. The method according to claim 1 whereincorrelating the context features comprises correlating context featuresthat are dynamic over time.
 4. The method according to claim 1 whereinaggregating the plurality of the multiple entities comprises filteringthe correlated context features to provide relevant information to aninformation consumer.
 5. The method according to claim 1 whereincorrelating the context features comprises correlating the contextfeatures based on a specific purpose defined by an information consumer.6. The method according to claim 5 further comprising using changingcontextual correlations to predict future information needs for theinformation consumer.
 7. The method according to claim 1 whereincorrelating the context features comprises correlating the contextfeatures to identify and manage relationship among the plurality of themultiple entities.
 8. The method according to claim 1 whereincorrelating the context features comprises leveraging existingcontextual ontologies with mappings and conversions represented by bothontological predicates and pluggable reasoning services.
 9. The methodaccording to claim 1 wherein aggregating the plurality of the multipleentities comprises calculating information relationships and composinginformation objects that merge the received information.
 10. The methodaccording to claim 1 wherein aggregating the plurality of the multipleentities comprises binding temporal and contextual information relevancyand any information regarding anticipated future context-based entityneeds.
 11. The method according to claim 1 wherein correlating thecontext features comprises using at least one of deterministic,probabilistic, and fuzzy logic matching algorithms to correlate thecontext features.
 12. The method according to claim 1 whereincorrelating the context features further comprises: utilizing ontologiesand algorithms to enrich the semantics of contextual content by taggingdata models and data instances with context ontologies; and providingvarying degrees of domain-customized generality and specialization toaddress dynamically changing information space.
 13. The method accordingto claim 1 wherein correlating the context features further comprises:using semantic rules to compose and decompose existing alignments withina hierarchical structure; and using semantic rules to append and relaxat least one of alignment conditions and confidence levels forapproximations, such that the adaptability and reliability of contextdata management and dissemination in dynamic environments is increased.14. One or more computer-readable storage media havingcomputer-executable instructions embodied thereon, wherein when executedby at least one processor, the computer-executable instructions causethe at least one processor to: receive information relating to aplurality of the entities, the information including data features andcontext features; separately correlate the data features and the contextfeatures; aggregate the plurality of entities based on a correspondenceof the data features, a correspondence of the context features, and atleast one context model that includes temporal information associatedwith the context features; receive a first contextual event includingtemporal information, wherein the first contextual event is associatedwith a first location; receive a second contextual event includingtemporal information, wherein the second contextual event is differentfrom the first contextual event, and the second contextual event isassociated with a second location different from the first location;match a plurality of entities based on the received contextual events;determine a correspondence value for the matched plurality of entitiesbased on a comparison of at least one of the data features or contextfeatures in light of the temporal information of the first contextualevent and the first location; dynamically update a correspondence valuefor the matched plurality of entities based on a comparison of at leastone of the data features or context features in light of the temporalinformation of the second contextual event and the second location; andoutput the matched plurality of entities that exceed a predeterminedcorrespondence value.
 15. One or more computer-readable storage mediaaccording to claim 14 wherein the computer-executable instructions causethe at least one processor to leverage existing contextual ontologieswith mappings and conversions represented by both ontological predicatesand pluggable reasoning services to correlate the contextual features.16. One or more computer-readable storage media according to claim 14wherein the computer-executable instructions cause the at least oneprocessor to use at least one of deterministic, probabilistic, and fuzzylogic matching algorithms to correlate the context features.
 17. One ormore computer-readable storage media according to claim 14 wherein thecomputer-executable instructions cause the at least one processor tocorrelate the context features such that relationships among theplurality of entities are identified and managed.